nlp engineer

Almaimana Accounting and Bookkeeping

<p><b>Location:</b> Remote / Dubai, UAE (Project-Based)<br/><b>Employment Type:</b> Freelance / Contract<br/><b>Department:</b> Technology &amp; Digital Innovation</p><p>Job Summary</p><p>We are seeking a talented and experienced Freelance AI Engineer to design, develop, and implement Artificial Intelligence (AI) solutions on a project basis. The ideal candidate will have hands-on experience in Machine Learning, Generative AI, Natural Language Processing (NLP), AI automation, and Large Language Models (LLMs). The role requires the ability to work independently, manage project timelines, and deliver scalable AI solutions aligned with business objectives.</p><p>Key Responsibilities</p><ul><li>Develop and deploy AI-powered applications, chatbots, virtual assistants, and automation solutions.</li><li>Build and fine-tune Machine Learning and Deep Learning models.</li><li>Design and implement Generative AI solutions using Large Language Models (LLMs).</li><li>Integrate AI solutions with websites, mobile applications, ERP systems, and business platforms.</li><li>Develop Retrieval-Augmented Generation (RAG) systems and AI agents.</li><li>Analyze business requirements and recommend suitable AI technologies.</li><li>Prepare technical documentation and deployment guides.</li><li>Conduct testing, troubleshooting, and optimization of AI applications.</li><li>Ensure data security, privacy, and compliance with industry best practices.</li><li>Provide post-deployment support and maintenance when required.</li></ul><p>Required Qualifications</p><ul><li>Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related field (preferred).</li><li>Proven experience in AI/ML development and deployment.</li><li>Strong proficiency in Python and AI development frameworks.</li><li>Experience with Generative AI platforms and APIs.</li><li>Knowledge of NLP, Machine Learning, and Deep Learning techniques.</li><li>Experience integrating AI solutions through APIs and cloud services.</li><li>Strong problem-solving and communication skills.</li><li>Ability to work independently and meet project deadlines.</li></ul><p>Technical Skills</p><ul><li>Python</li><li>TensorFlow, PyTorch, Scikit-learn</li><li>OpenAI, Gemini, Claude, or similar AI platforms</li><li>LangChain, LlamaIndex</li><li>RAG Architecture and AI Agents</li><li>SQL and NoSQL Databases</li><li>Vector Databases (Pinecone, ChromaDB, Weaviate)</li><li>REST APIs</li><li>Docker and Cloud Platforms (AWS, Azure, GCP)</li><li>Git Version Control</li></ul><p>Preferred Experience</p><ul><li>AI Chatbot Development</li><li>Intelligent Document Processing (IDP)</li><li>OCR and Computer Vision Applications</li><li>Business Process Automation</li><li>Predictive Analytics and Data Science Projects</li><li>MLOps and Model Deployment</li></ul><p>Deliverables</p><p>The selected freelancer may be required to:</p><ul><li>Develop custom AI applications.</li><li>Create AI-powered customer support solutions.</li><li>Automate business processes using AI.</li><li>Build intelligent document management systems.</li><li>Integrate AI models with existing software platforms.</li><li>Provide training and knowledge transfer to internal teams.</li></ul><p>Contract Terms</p><ul><li>Project-based or hourly engagement.</li><li>Flexible working hours.</li><li>Remote work arrangement.</li><li>Payment based on agreed milestones and deliverables.</li><li>Opportunity for long-term collaboration on future AI projects.</li></ul><p>How to Apply</p><p>Interested candidates should submit:</p><ul><li>Updated CV/Resume</li><li>Portfolio of AI projects</li><li>GitHub or relevant technical profiles</li></ul><p><b>Only candidates with demonstrated experience in AI development and successful project delivery will be considered.</b></p><p>Application Question(s):</p><ul><li>What is your expected Salary?</li><li>Can you join immediately after selection?</li></ul><p>Education:</p><ul><li>Bachelor's (Preferred)</li></ul><p>Work Location: In person</p>

Freelance AI Engineer

Apple

<p>The Apple Services Engineering AI/ML organization is hiring a Machine Learning Engineer to join the Search team for Video catalog.<br>
<br>
Our team builds the core intelligence that powers search discovery experiences in the Apple TV App, Siri, and Spotlight cross platforms, helping users effortlessly find and enjoy the content they love. We are a collaborative, high-impact team that values innovation, craftsmanship, and end-to-end ownership from idea to launch. Our systems combine large-scale data, modern retrieval and ranking models, and a deep commitment to user privacy.<br>
<br>
Join us, you&rsquo;ll develop scalable systems and machine learning models that drive search relevance, personalization, and understanding of content at scale. Working closely with cross-functional partners in product and design, you&rsquo;ll translate cutting-edge research in advanced machine learning and generative AI into secure and delightful production features used by millions every day.<br>
<br>
<b>Description</b><br>
<br>
As a Machine Learning Engineer on the Video Search team, within the Apple Services Engineering AI/ML organization, you will design and deploy large-scale ML systems that power search and discovery across Apple platforms.<br>
<br>
You&rsquo;ll apply machine learning, natural language understanding, and generative AI to model user intent and deliver relevant, personalized results. Your work will involve building and optimizing cutting edge data processing, ML models, retrieval pipelines, and ranking systems that operate at global scale and under strict privacy standards.<br>
<br>
This is a hands-on role where you will collaborate closely with cross-functional teams to bring advanced ML technologies into production-shaping how users discovery content they love in Apple TV app, cross Apple TV partners on Apple Platforms, also through Siri and Spotlight.&quot;,&quot;responsibilities&quot;:&quot;Solve complex research problems and implement solutions from concept to execution.<br>
<br>
Design and implement retrieval and ranking systems using semantics and user context.<br>
<br>
Build and deploy ML, NLP and LLM models to improve search relevance and personalization.<br>
<br>
Analyze data and model performance to identify opportunities for search quality enhancement.<br>
<br>
Develop automated tests for continuous integration and ensure successful production deployment.<br>
<br>
Conduct A/B tests to measure search improvements.<br>
<br>
Collaborate with cross-functional teams to innovate intelligent video catalog text search.<br>
<br>
Enhance search recall and ranking for global Apple devices across all platforms and languages.<br>
<br>
Utilize big data tech to evaluate content discovery features.<br>
<br>
Ensure systems meet Apple&rsquo;s privacy, efficiency, and user experience standards.<br>
<br>
<b>Preferred Qualifications</b><br>
<br>
Experience with search or recommendation systems, and semantic retrieval or vector databases.<br>
<br>
Expertise in transformer architectures, embeddings, and retrieval or ranking models.<br>
<br>
Experience in applying or fine-tuning LLMs for understanding and generation tasks. Familiarity with prompt design, context management, RAG and Agentic architectures and solutions.<br>
<br>
Exposure to evaluation and safety frameworks for LLM-based systems.<br>
<br>
Knowledge of reinforcement learning and other modern post training practices for LLMs.<br>
<br>
Passion for developing intelligent, human-centered experiences to enhance content discovery.<br>
<br>
<b>Minimum Qualifications</b><br>
<br>
Experience in machine learning, NLP, IR, or more recently Large Language Model ( LLMs).<br>
<br>
Strong programming skills in Python, Java and Go for building scalable ML systems.<br>
<br>
Hands-on expertise in ML libraries such as PyTorch, JAX, TensorFlow for model training and deployment.<br>
<br>
Ability to translate product goals into technical solutions, improving user experience.<br>
<br>
Strong communication, collaboration, and analytical problem-solving skills.<br>
<br>
In-depth knowledge of search and information retrieval fundamentals, including indexing and ranking. Experience with retrieval and ranking algorithms and building big data pipelines using Hadoop, Java, Scala, Spark and more.<br>
<br>
Industrial experience in search, classification, recommendation systems, or related fields.<br>
<br>
Familiarity with A/B testing and data-driven product development.<br>
<br>
Passionate about creating products loved by customers at Apple.<br>
<br>
Bachelor&rsquo;s degree or higher (or equivalent practical experience) in Computer Science, Machine Learning, Natural Language Processing, Artificial Intelligence, or a related field.<br>
<br>
<b>Pay &amp; Benefits</b><br>
<br>
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.<br>
<br>
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits<br>
<br>
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.</p>

Machine Learning Engineer, ASE Search Team

Spotify

<div><div><div><div><div><div>Engineering</div></div></div><div><div>
Machine Learning</div></div></div><div><div>
Permanent</div></div><div><div>
New York</div></div><div><div><div>
<div>We design Spotify&rsquo;s consumer experience&mdash;end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints&mdash;from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.<br>
<br>
The Verbatim squad sits within the Enrichment &amp; Content Intelligence product area and is focused on helping Spotify better understand audio, text, and visual content through machine learning. The team develops technologies that power experiences across Spotify including content skipping, transcription, moderation, and visual understanding. Working at the intersection of large-scale machine learning and product innovation, the squad partners closely with Product, Engineering, and Data Science teams to build intelligent systems that improve how users experience content across the platform.</div>
</div></div></div></div><div><div><p>
What You'll Do</p><ul><div>
<ul>
<li>Lead end-to-end machine learning initiatives from ideation and prototyping through experimentation, deployment, and large-scale productionization.</li>
<li>Design, develop, and deploy machine learning systems that operate across hundreds of millions of content signals using both real-time and batch processing architectures.</li>
<li>Advance Spotify&rsquo;s capabilities in natural language understanding, multimodal AI, and content intelligence.<br>
Build and evaluate LLM-powered solutions using modern prompting techniques, retrieval systems, and advanced model orchestration approaches.</li>
<li>Define rigorous evaluation methodologies including golden datasets, precision and recall frameworks, offline benchmarking, and online experimentation.</li>
<li>Partner closely with Product Managers, Engineering Managers, Staff Engineers, and Data Scientists to influence technical strategy and roadmap decisions.</li>
<li>Mentor engineers across the organization and help elevate machine learning engineering standards and best practices.</li>
<li>Contribute to the adoption of AI-assisted development workflows and tooling that improve team productivity and engineering effectiveness.</li>
</ul>
</div></ul></div></div><div><div><p>Who You Are</p><ul><div>
<ul>
<li>You have solid experience developing and deploying machine learning systems in production environments.</li>
<li>You have successfully delivered large-scale machine learning architectures operating on substantial datasets and high-throughput production systems.</li>
<li>You have deep experience with machine learning, deep learning, and modern AI technologies.</li>
<li>You have hands-on experience working with large language models and understand how to evaluate, adapt, and deploy them effectively for real-world product challenges.</li>
<li>You have experience building evaluation frameworks and can quantify model performance through robust experimentation and measurement techniques.</li>
<li>You know how to navigate ambiguity and make thoughtful technical trade-offs that balance product impact, scalability, and engineering quality.</li>
<li>You have experience influencing technical direction across cross-functional teams and can communicate complex machine learning concepts to diverse audiences.</li>
<li>You care about developing others and enjoy mentoring engineers through technical guidance and collaboration.</li>
<li>You have experience working with NLP, prompt engineering, retrieval-augmented generation (RAG), vector databases, or multimodal machine learning systems.</li>
<li>You are curious about emerging AI technologies and excited about integrating tools such as Claude Code, Cursor, and other AI-assisted development capabilities into engineering workflows.</li>
</ul>
</div></ul></div></div><div><div><p>Where You'll Be</p><ul><div>
<ul>
<li>This role is based in New York City</li>
<li>We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.</li>
</ul>
</div></ul></div></div><div><div><p>Learn about life at Spotify</p></div><div><div>
<div>
The United States base range for this position is $184,049&ndash;262,928 USD, plus equity. The benefits available for this position include health insurance, six-month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, paid flexible holidays, and paid sick leave. These ranges may be modified in the future.</div>
</div></div></div><div><div><p>
Our global benefits</p></div><div></div><div><div>
Extensive learning opportunities, through our dedicated team, GreenHouse.</div></div><div></div><div><div>
Flexible share incentives letting you choose how you share in our success.</div></div><div></div><div><div>
Global parental leave, six months off - for all new parents.</div></div><div></div><div><div>
All The Feels, our employee assistance program and self-care hub.</div></div><div></div><div><div>
Flexible public holidays, swap days off according to your values and beliefs.</div></div></div></div><div></div>

Senior Machine Learning Engineer, Content Intelligence

Diverse Agile Solutions

<div><p>Conversational AI Developer (Chatbot Developer)</p><p></p><p><br>
Position Overview</p>
<p>
Diverse Agile Solutions is seeking a talented and passionate <b>Conversational AI Developer</b> to play a key role in managing existing chatbot implementations while designing and delivering next-generation conversational AI experiences. This position bridges traditional Natural Language Understanding (NLU) platforms with modern Generative AI and agent-based architectures.</p>
<p>
The ideal candidate will possess deep technical expertise within the Microsoft conversational ecosystem, strong enterprise integration experience, and a passion for creating intuitive, human-centered digital experiences. This individual will help drive the evolution of our conversational platforms from traditional chatbot frameworks toward advanced AI agent solutions leveraging Microsoft AI technologies.</p>
<p>
Key Responsibilities</p>
<p>
Conversational AI Development</p>
<ul>
<li>Design, develop, test, deploy, and maintain enterprise-grade chatbots, virtual assistants, and AI-powered conversational agents.</li>
<li>Manage the full development lifecycle, from requirements gathering and conversational design through production deployment and ongoing optimization.</li>
<li>Build scalable and secure conversational solutions across web, mobile, and messaging platforms.</li>
</ul>
<p>Platform Architecture &amp; Modernization</p>
<ul>
<li>Maintain and optimize existing Azure Bot Framework SDK v4 implementations.</li>
<li>Contribute to architectural modernization initiatives involving Microsoft Copilot Studio, Azure AI Foundry, and agent-based frameworks.</li>
<li>Evaluate emerging conversational AI technologies and recommend enhancements to existing platforms.</li>
</ul>
<p>Enterprise Systems Integration</p>
<ul>
<li>Design and implement integrations between conversational interfaces and enterprise applications using MuleSoft APIs and integration services.</li>
<li>Connect chatbot solutions with backend systems such as:
<ul><li>PeopleSoft</li>
<li>Salesforce</li>
<li>Knowledge Management Platforms</li>
<li>Enterprise Databases and Services</li>
</ul></li><li>Develop secure APIs and middleware services supporting conversational workflows.</li>
</ul>
<p>Omnichannel Experience Management</p>
<ul>
<li>Create and manage dynamic conversational interfaces utilizing:
<ul><li>Microsoft Adaptive Cards</li>
<li>Knowledge Base Integrations</li>
<li>Workflow Automation Tools</li>
<li>Web and Mobile Chat Interfaces</li>
</ul></li><li>Deliver seamless user experiences across multiple communication channels.</li>
</ul>
<p>Live Agent Escalation &amp; Case Management</p>
<ul>
<li>Implement intelligent escalation workflows from automated assistants to live support agents.</li>
<li>Integrate conversational systems with Salesforce Live Chat and Salesforce Case Management solutions.</li>
<li>Develop automated ticket creation, routing, and case management capabilities.</li>
</ul>
<p>AI, NLU &amp; LLM Orchestration</p>
<ul>
<li>Develop hybrid AI solutions leveraging:
<ul><li>Azure Conversational Language Understanding (CLU)</li>
<li>Azure OpenAI Services</li>
<li>GPT-based models</li>
<li>Retrieval-Augmented Generation (RAG) patterns</li>
</ul></li><li>Design intent recognition, entity extraction, contextual memory, and fallback handling strategies.</li>
<li>Optimize prompt engineering and AI orchestration workflows to improve response quality and user satisfaction.</li>
</ul>
<p>Analytics &amp; Continuous Improvement</p>
<ul>
<li>Monitor chatbot performance metrics including:
<ul><li>Containment Rates</li>
<li>Intent Accuracy</li>
<li>User Satisfaction</li>
<li>Conversation Completion Rates</li>
<li>Escalation Metrics</li>
</ul></li><li>Analyze conversation logs and user feedback to identify optimization opportunities.</li>
<li>Implement improvements that enhance accuracy, reduce hallucinations, and improve overall user engagement.</li>
</ul>
<p>Collaboration &amp; Stakeholder Engagement</p>
<ul>
<li>Collaborate closely with Product Owners, UX Designers, Business Analysts, Developers, and business stakeholders.</li>
<li>Translate complex business requirements into effective conversational experiences.</li>
<li>Communicate technical concepts and AI limitations clearly to both technical and non-technical audiences.</li>
</ul>
<p>Required Qualifications</p>
<p>
Technical Experience</p>
<ul>
<li>Bachelor's degree in Computer Science, Information Technology, Software Engineering, or a related field (or equivalent professional experience).</li>
<li>Minimum <b>3 years of experience</b> developing software applications with a focus on:
<ul><li>Conversational AI</li>
<li>Chatbots</li>
<li>NLP/NLU systems</li>
<li>AI Assistants</li>
<li>Generative AI Solutions</li>
</ul>
</li></ul><p>Microsoft Conversational Technologies</p>
<ul>
<li>Strong hands-on experience with:
<ul><li>Azure Bot Framework SDK v4</li>
<li>Waterfall Dialogs</li>
<li>Middleware Development</li>
<li>Conversation State Management</li>
</ul></li><li>Experience with or strong understanding of:
<ul><li>Microsoft Copilot Studio</li>
<li>Azure AI Foundry</li>
<li>Agentic AI architectures</li>
</ul>
</li></ul><p>Programming &amp; Development</p>
<ul>
<li>Advanced proficiency in:
<ul><li>C#</li>
<li>.NET</li>
<li>JavaScript</li>
</ul></li><li>Working knowledge of:
<ul><li>HTML</li>
<li>CSS</li>
<li>REST APIs</li>
<li>JSON-based integrations</li>
</ul>
</li></ul><p>Enterprise Integration</p>
<ul>
<li>Experience integrating conversational solutions with enterprise systems through:
<ul><li>MuleSoft</li>
<li>Salesforce APIs</li>
<li>PeopleSoft</li>
<li>Other enterprise application platforms</li>
</ul>
</li></ul><p>Preferred Qualifications</p>
<ul>
<li>Experience with Azure Cosmos DB for conversation state management and telemetry storage.</li>
<li>Familiarity with:
<ul><li>Azure Application Insights</li>
<li>Power BI</li>
<li>Data visualization and chatbot analytics platforms</li>
</ul></li><li>Experience implementing CI/CD pipelines for chatbot and AI deployments.</li>
<li>Knowledge of:
<ul><li>Azure DevOps</li>
<li>GitHub Actions</li>
<li>Automated Testing Frameworks</li>
</ul></li><li>Experience building Retrieval-Augmented Generation (RAG) solutions.</li>
<li>Understanding of AI governance, responsible AI principles, and prompt engineering best practices.</li>
<li>Experience supporting Higher Education, Government, Healthcare, or other large enterprise environments.</li>
<li>Microsoft Azure certifications are highly desirable.</li>
</ul>
<p>Desired Skills</p>
<ul>
<li>Strong analytical and problem-solving abilities.</li>
<li>Excellent verbal and written communication skills.</li>
<li>Ability to work independently and collaboratively within Agile teams.</li>
<li>Passion for innovation and emerging AI technologies.</li>
<li>Strong customer-focused mindset and commitment to delivering exceptional user experiences.</li>
</ul>
<p>Why Join Diverse Agile Solutions?</p>
<p>
Cutting-Edge Innovation</p>
<p>
Work on transformative AI initiatives utilizing the latest advancements in Generative AI, Agentic AI, Microsoft Copilot technologies, and enterprise automation.</p>
<p>
Meaningful Impact</p>
<p>
Develop solutions that directly improve customer, employee, and stakeholder experiences while supporting high-volume business operations.</p>
<p>
Professional Growth</p>
<p>
Expand your expertise in:</p>
<ul>
<li>Generative AI</li>
<li>Large Language Models (LLMs)</li>
<li>AI Agent Frameworks</li>
<li>Microsoft AI Technologies</li>
<li>Enterprise Cloud Solutions</li>
</ul>
<p>Collaborative Culture</p>
<p>
Join a team of passionate professionals dedicated to innovation, continuous learning, and delivering exceptional results for our clients.</p>
<p>
Employment Information</p>
<ul>
<li><b>Job Title:</b> Conversational AI Developer (Chatbot Developer)</li>
<li><b>Employment Type:</b> Full-Time</li>
<li><b>Location:</b> Remote / Hybrid (Based on Client Requirements)</li>
<li><b>Travel:</b> Minimal</li>
<li><b>Work Authorization:</b> Must be authorized to work in the United States</li>
<li><b>Clearance:</b> May be required depending on client engagement</li>
</ul>
<p>Equal Opportunity Employer</p>
<p>
Diverse Agile Solutions is an Equal Opportunity Employer committed to fostering an inclusive workplace where all employees are valued and respected. We celebrate diversity and are dedicated to creating an environment where everyone can thrive and contribute their unique perspectives.</p>
<p><b>
Interested candidates are encouraged to apply and join DAS in shaping the future of intelligent conversational experiences.</b></p></div>

Chatbot Developer

Datamatics Global Services Ltd

<div><b>Position Title : AI/ML Engineer &ndash; Agentic AI</b><br>
<b>Experience Required: </b>4&ndash;6 Years (Should be open to Travel)<br>
<b>Employment Type : </b>Full-Time - Riyadh - Onsite<br>
<br>
<b>Job Overview</b><br>
We are seeking a highly skilled AI/ML Engineer with hands-on experience in building intelligent AI systems, machine learning models, and Agentic AI solutions. The ideal candidate will have strong expertise in designing, developing, and deploying AI-powered applications leveraging LLMs, autonomous agents, orchestration frameworks, and modern ML techniques.<br>
The role requires a strong engineering mindset, practical AI implementation experience, and the ability to work across the full AI/ML lifecycle from experimentation to production deployment.<br>
<br>
<b>Key Responsibilities</b><ul><li>
Design, develop, and deploy AI/ML solutions for real-world business use cases.</li><li>
Build and optimize <b>Agentic AI systems</b> using LLMs, autonomous workflows, and multi-agent architectures.</li><li>
Develop machine learning models for prediction, classification, recommendation, NLP, and automation tasks.</li><li>
Work with orchestration frameworks such as LangChain, CrewAI, AutoGen, LangGraph, or similar technologies.</li><li>
Integrate AI agents with APIs, databases, enterprise systems, and third-party tools.</li><li>
Fine-tune, evaluate, and optimize Large Language Models (LLMs).</li><li>
Develop Retrieval-Augmented Generation (RAG) pipelines and vector search implementations.</li><li>
Collaborate with data engineers, software developers, and product teams to deliver scalable AI solutions.</li><li>
Perform prompt engineering, model experimentation, and performance optimization.</li><li>
Ensure AI solutions are production-ready, scalable, secure, and cost-efficient.</li><li>
Monitor model performance, drift, and continuous improvement processes.</li><li>
Stay updated with emerging AI, Generative AI, and Agentic AI trends and technologies.</li></ul><b>
Required Skills &amp; Expertise</b><ul><li>
Strong hands-on experience in <b>Machine Learning, Generative AI, and Agentic AI development</b>.</li><li>
Proficiency in Python and AI/ML development frameworks.</li><li>
Experience with:<ul><li>
LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks</li><li>
OpenAI, Anthropic, Gemini, or open-source LLMs</li><li>
RAG architectures and Vector Databases</li><li>
Prompt Engineering &amp; AI workflow automation</li></ul></li><li>
Solid understanding of:<ul><li>
NLP concepts</li><li>
ML algorithms and model evaluation</li><li>
Deep Learning fundamentals</li><li>
AI model deployment and inference optimization</li></ul></li><li>
Experience with ML libraries/frameworks such as:<ul><li>
PyTorch</li><li>
TensorFlow</li><li>
Scikit-learn</li><li>
Hugging Face Transformers</li></ul></li><li>
Experience with cloud platforms such as Azure, AWS, or GCP is preferred.</li><li>
Familiarity with Docker, APIs, CI/CD, and MLOps practices.</li><li>
Strong problem-solving and analytical skills.</li></ul><b>
Preferred Qualifications</b><ul><li>
Bachelor&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science, or related field.</li><li>
Experience working on enterprise AI automation or AI assistant platforms is highly preferred.</li><li>
Exposure to AI governance, evaluation frameworks, and AI observability tools is a plus.</li></ul><br>
<p>
</p><p>tTAmIvfFVv</p></div>

AI/ML Engineer – Agentic AI

Deloitte

<div>
 Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort,, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system decides - across payers, providers, and life sciences, and for the patients they serve - so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.
 <br/>
 <br/>
 This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes.
 <br/>
 <br/>
 As a Research Engineer on our post-training team, you will design, train, evaluate, and align the models that reason about healthcare - working across the full post-training lifecycle to shape model behavior for clinical and operational decisioning across the industry. Healthcare decisioning is one of the cleanest verifiable-reward domains outside math and code: the problems are hard. We ground that reward in real signals - clinical policy and criteria, adjudicated outcomes, and clinical-expert judgment - so correctness is checkable rather than asserted.
 <br/>
 <br/>
 You will own the post-training stack for our clinical reasoning models end to end - from data and reward design through trained, evaluated models that ship. This is not a prompt-engineering role. We are looking for people who understand not just how to use LLMs, but how to improve and shape model behavior through advanced post-training.
 <br/>
 <br/>
 You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain - you bring the modeling depth.
 <br/>
 <br/>
 We hire on demonstrated depth, not years - the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.
 <br/>
 <br/>
 Work you'll do
 <br/>
 <br/>
 Post-training &amp; alignment
 <br/>
 <br/>
 <ul>
  <li>
   Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning / alignment workflows.
  </li>
  <li>
   Build and optimize training using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, and Constitutional AI, and understand how each affects reasoning quality, safety, latency, cost, and reliability.
  </li>
  <li>
   Train reasoning models for healthcare decisioning using verifiable-reward RL - designing reward signals and verifiers grounded in clinical guidelines, policy and criteria, and adjudicated outcomes.
   <br/>
   <br/>
  </li>
 </ul>
 Reward modeling &amp; data
 <br/>
 <br/>
 <ul>
  <li>
   Develop reward models and preference datasets to improve reasoning quality, factuality, safety, policy adherence, and task performance.
  </li>
  <li>
   Curate, clean, synthesize, and evaluate large-scale instruction, preference, and domain-specific datasets, with rigorous filtering, deduplication, and quality control.
  </li>
  <li>
   Build verification and reward pipelines from our proprietary clinical, claims, and operational data and from clinical-expert labeling - turning guidelines, policy, and adjudicated outcomes into checkable reward signals at scale.
   <br/>
   <br/>
  </li>
 </ul>
 Efficient fine-tuning, training &amp; inference infrastructure
 <br/>
 <br/>
 <ul>
  <li>
   Implement efficient fine-tuning strategies including LoRA, QLoRA, PEFT, and adapter-based approaches; build scalable distributed training using DeepSpeed, FSDP, Megatron-LM, Ray, or equivalent.
  </li>
  <li>
   Optimize inference performance - latency, throughput, quantization, and deployment efficiency - for production, including frameworks such as vLLM, TensorRT-LLM, or TGI.
   <br/>
   <br/>
  </li>
 </ul>
 Small language models &amp; open-weight models
 <br/>
 <br/>
 <ul>
  <li>
   Train and optimize open-weight models such as Llama, Qwen, Mistral, or DeepSeek; build specialized small language models (SLMs) for on-premise and cloud-hybrid deployment with strong performance-per-dollar.
   <br/>
   <br/>
  </li>
 </ul>
 Evaluation, safety &amp; red teaming
 <br/>
 <br/>
 <ul>
  <li>
   Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.
  </li>
  <li>
   Build healthcare-grade evaluation - held-out clinical benchmarks, deployment regression gates, calibration and uncertainty, factuality against ground truth, and bias/fairness evaluation across patient populations and subgroups - co-designed with clinical experts.
  </li>
  <li>
   Apply PHI/HIPAA-aware data handling and produce model documentation suitable for regulated clinical use.
  </li>
  <li>
   Perform red teaming and adversarial testing to identify alignment failures, unsafe behaviors, jailbreak vulnerabilities, and regression risks; collaborate with agentic and application teams to improve tool use, grounding, and long-horizon reasoning.
   <br/>
   <br/>
  </li>
 </ul>
 The team
 <br/>
 <br/>
 Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world. The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.
 <br/>
 <br/>
 You can go deep. The team sub-specializes across post-training research, data and reward engineering, and training and inference infrastructure - you won't be expected to own all of it alone.
 <br/>
 <br/>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.
  </li>
  <li>
   Demonstrated depth training and post-training large transformer-based language models in production or research - this is your craft, not coursework or a one-off fine-tune. Genuine depth including SFT and at least one preference-optimization or RL method, evidenced by shipped models, releases, or research.
  </li>
  <li>
   Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.
  </li>
  <li>
   Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.
  </li>
  <li>
   Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.
  </li>
  <li>
   Strong expertise in PyTorch and modern deep-learning tooling; experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Ray.
  </li>
  <li>
   Experience implementing efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and quantization-aware workflows.
  </li>
  <li>
   Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.
  </li>
  <li>
   Strong grasp of LLM evaluation methodologies, benchmarking, reward modeling, and alignment trade-offs; experience with large-scale and synthetic datasets, filtering, deduplication, and quality-control pipelines.
  </li>
  <li>
   Strong Python engineering skills and production-grade software practices; ability to work through ambiguous, highly complex technical problems in fast-moving environments.
  </li>
  <li>
   Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.
  </li>
  <li>
   Limited immigration sponsorship may be available.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.
  </li>
  <li>
   Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.
  </li>
  <li>
   Experience with multimodal models, speech models, or domain-specific foundation models; experience using large-scale GPU clusters and distributed compute.
  </li>
  <li>
   Contributions to open-source AI projects, research publications, benchmark development, or model releases.
  </li>
  <li>
   Familiarity with safety, governance, and responsible-AI practices; experience in regulated or high-stakes industries such as healthcare, finance, insurance, or public sector.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Compensation
  <br/>
  <br/>
 </strong>
 Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance-based incentive opportunity designed to grow with the value you help create - startup-style upside, with the backing of a committed, well-capitalized platform. The estimated base salary range is $134,500-$265,100 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.
</div>


Research Engineer — Post-Training & Small Language Models (SLMs), Healthcare AI

<div>Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort,, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system decides - across payers, providers, and life sciences, and for the patients they serve - so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.<br>
<br>
This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes.<br>
<br>
As a Research Engineer on our post-training team, you will design, train, evaluate, and align the models that reason about healthcare - working across the full post-training lifecycle to shape model behavior for clinical and operational decisioning across the industry. Healthcare decisioning is one of the cleanest verifiable-reward domains outside math and code: the problems are hard. We ground that reward in real signals - clinical policy and criteria, adjudicated outcomes, and clinical-expert judgment - so correctness is checkable rather than asserted.<br>
<br>
You will own the post-training stack for our clinical reasoning models end to end - from data and reward design through trained, evaluated models that ship. This is not a prompt-engineering role. We are looking for people who understand not just how to use LLMs, but how to improve and shape model behavior through advanced post-training.<br>
<br>
You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain - you bring the modeling depth.<br>
<br>
We hire on demonstrated depth, not years - the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.<br>
<br>
Work you'll do<br>
<br>
Post-training &amp; alignment<br>
<br>
<ul><li>Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning / alignment workflows.</li></ul><br>
<ul><li>Build and optimize training using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, and Constitutional AI, and understand how each affects reasoning quality, safety, latency, cost, and reliability.</li></ul><br>
<ul><li>Train reasoning models for healthcare decisioning using verifiable-reward RL - designing reward signals and verifiers grounded in clinical guidelines, policy and criteria, and adjudicated outcomes.</li></ul><br>
Reward modeling &amp; data<br>
<br>
<ul><li>Develop reward models and preference datasets to improve reasoning quality, factuality, safety, policy adherence, and task performance.</li></ul><br>
<ul><li>Curate, clean, synthesize, and evaluate large-scale instruction, preference, and domain-specific datasets, with rigorous filtering, deduplication, and quality control.</li></ul><br>
<ul><li>Build verification and reward pipelines from our proprietary clinical, claims, and operational data and from clinical-expert labeling - turning guidelines, policy, and adjudicated outcomes into checkable reward signals at scale.</li></ul><br>
Efficient fine-tuning, training &amp; inference infrastructure<br>
<br>
<ul><li>Implement efficient fine-tuning strategies including LoRA, QLoRA, PEFT, and adapter-based approaches; build scalable distributed training using DeepSpeed, FSDP, Megatron-LM, Ray, or equivalent.</li></ul><br>
<ul><li>Optimize inference performance - latency, throughput, quantization, and deployment efficiency - for production, including frameworks such as vLLM, TensorRT-LLM, or TGI.</li></ul><br>
Small language models &amp; open-weight models<br>
<br>
<ul><li>Train and optimize open-weight models such as Llama, Qwen, Mistral, or DeepSeek; build specialized small language models (SLMs) for on-premise and cloud-hybrid deployment with strong performance-per-dollar.</li></ul><br>
Evaluation, safety &amp; red teaming<br>
<br>
<ul><li>Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.</li></ul><br>
<ul><li>Build healthcare-grade evaluation - held-out clinical benchmarks, deployment regression gates, calibration and uncertainty, factuality against ground truth, and bias/fairness evaluation across patient populations and subgroups - co-designed with clinical experts.</li></ul><br>
<ul><li>Apply PHI/HIPAA-aware data handling and produce model documentation suitable for regulated clinical use.</li></ul><br>
<ul><li>Perform red teaming and adversarial testing to identify alignment failures, unsafe behaviors, jailbreak vulnerabilities, and regression risks; collaborate with agentic and application teams to improve tool use, grounding, and long-horizon reasoning.</li></ul><br>
The team<br>
<br>
Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world. The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.<br>
<br>
You can go deep. The team sub-specializes across post-training research, data and reward engineering, and training and inference infrastructure - you won't be expected to own all of it alone.<br>
<br>
Required qualifications<br>
<br>
<ul><li>Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.</li></ul><br>
<ul><li>Demonstrated depth training and post-training large transformer-based language models in production or research - this is your craft, not coursework or a one-off fine-tune. Genuine depth including SFT and at least one preference-optimization or RL method, evidenced by shipped models, releases, or research.</li></ul><br>
<ul><li>Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.</li></ul><br>
<ul><li>Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.</li></ul><br>
<ul><li>Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.</li></ul><br>
<ul><li>Strong expertise in PyTorch and modern deep-learning tooling; experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Ray.</li></ul><br>
<ul><li>Experience implementing efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and quantization-aware workflows.</li></ul><br>
<ul><li>Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.</li></ul><br>
<ul><li>Strong grasp of LLM evaluation methodologies, benchmarking, reward modeling, and alignment trade-offs; experience with large-scale and synthetic datasets, filtering, deduplication, and quality-control pipelines.</li></ul><br>
<ul><li>Strong Python engineering skills and production-grade software practices; ability to work through ambiguous, highly complex technical problems in fast-moving environments.</li></ul><br>
<ul><li>Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.</li></ul><br>
<ul><li>Limited immigration sponsorship may be available.</li></ul><br>
Preferred qualifications<br>
<br>
<ul><li>Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.</li></ul><br>
<ul><li>Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.</li></ul><br>
<ul><li>Experience with multimodal models, speech models, or domain-specific foundation models; experience using large-scale GPU clusters and distributed compute.</li></ul><br>
<ul><li>Contributions to open-source AI projects, research publications, benchmark development, or model releases.</li></ul><br>
<ul><li>Familiarity with safety, governance, and responsible-AI practices; experience in regulated or high-stakes industries such as healthcare, finance, insurance, or public sector.</li></ul><br>
Compensation<br>
<br>
Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance-based incentive opportunity designed to grow with the value you help create - startup-style upside, with the backing of a committed, well-capitalized platform. The estimated base salary range is $134,500-$265,100 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.</div>

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Research Engineer — Post-Training & Small Language Models (SLMs), Healthcare AI

Research Engineer — Post-Training & Small Language Models (SLMs), Healthcare AI